Sensor data to identify catastrophe areas

ABSTRACT

A computer-implemented method for generating an automated response to a catastrophic event, that includes (1) analyzing a sample set of data generated in association with a catastrophic event to determine a threshold pattern; (2) receiving, with customer permission or affirmative consent, home sensor data from a smart home controller via wireless communication or data transmission, the home sensor data including data regarding at least one of (i) structural status; (ii) wind speed; (iii) availability of electricity; (iv) presence of water; (v) temperature; (vi) pressure; and/or (vii) presence of pollutants in the air and/or water; (3) determining, based upon or from computer analysis of the home sensor data, whether the home sensor data indicates a match to the threshold pattern; and (4) automatically generating a response if the home sensor data indicates a match to the threshold pattern. As a result, catastrophic events and responses thereto may be improved through usage of a remote network of home sensors.

RELATED APPLICATIONS

The current patent application is a continuation of, and claims thebenefit of, U.S. patent application Ser. No. 16/375,054, entitled“Sensor Data to Identify Catastrophe Areas” and filed Apr. 4, 2019,which is a continuation of, and claims the benefit of, U.S. patentapplication Ser. No. 16/211,525 (now U.S. Pat. No. 10,304,313), entitled“Sensor Data to Identify Catastrophe Areas” and filed Dec. 6, 2018,which is a continuation of, and claims the benefit of, U.S. patentapplication Ser. No. 15/904,769 (now U.S. Pat. No. 10,186,134), entitled“Sensor Data to Identify Catastrophe Areas” and filed Feb. 26, 2018,which is a continuation patent application which claims priority benefitwith regard to all common subject matter to identically-titled U.S.patent application Ser. No. 15/397,277 (now U.S. Pat. No. 9,947,202),filed Jan. 3, 2017, which, itself, claims priority benefit with regardto all common subject matter to: U.S. Provisional Application Ser. No.62/307,101, filed Mar. 11, 2016; and U.S. Provisional Application Ser.No. 62/275,566, filed Jan. 6, 2016. The listed earlier-filednon-provisional applications and provisional applications are herebyincorporated by reference in their entireties into the current patentapplication.

FIELD OF THE INVENTION

The present disclosure generally relates to devices and methods forusing sensor data to identify catastrophe areas.

BACKGROUND

Detection of and response to catastrophic events traditionally relies onpublic emergency detection and reporting sensor networks, such as theIntegrated Public Alert and Warning System and Emergency Alert System.However, such public infrastructure is designed specifically withcertain needs in mind namely, public safety and alert notification andmay not be optimized for other uses. Further, underlying data gatheredby public systems may not be made publically available in a usefulformat. Still further, public infrastructure may be underfunded, whichmay lead to incomplete data coverage with respect to geographical areasof interest and/or detectable event types. There is therefore a need foran improved system for detecting and responding to catastrophic events.

BRIEF SUMMARY

Embodiments of the present technology relate to computer-implementedmethods, computing devices, and computer-readable media for detectingand responding to catastrophic events. The embodiments provide forreceiving data generated by a plurality of sensors positioned in andaround a plurality of houses or other structures, comparing the dataagainst known thresholds or patterns indicative of catastrophic events,and generating responses and/or operational instructions if, forexample, a catastrophic event is indicated. The embodiments may permitimproved event detection and tracking for example where the systemautomatically reconfigures a remote sensor network and/or data gatheringregimes to improve data clarity around an event and/or may improve theefficiency and accuracy of remedial response(s) to the event.

In a first aspect, a computer-implemented method for tracking acatastrophic event using a remote sensor network is provided. The methodmay include receiving and comparing a first set of event data against anevent threshold to determine that the event threshold has been exceeded.A geographic boundary for an area associated with the first set of eventdata may also be identified. The boundary may encompass a plurality ofsensors positioned in and around a plurality of structures such ashouses. A data receipt pattern may also be determined based on the firstset of event data, and a data file may be initialized to receive asecond set of event data comprising sensor data from the plurality ofsensors based on the receipt pattern. The sensor data may then bereceived into the data file. As a result, data gathering regarding acatastrophic event may be improved, preferably through optimization ofsystem components and functions for an event type and use of “private”sensor networks in place of or in addition to public emergencynotification systems. The method may include additional, fewer, oralternative actions, including those discussed elsewhere herein.

In another aspect, a computer-implemented method for detecting andgenerating an automated response to a catastrophic event may beprovided. The method may include setting an event threshold that is fora first event type and that is configured for comparison against eventdata. The event data may be analyzed to determine that the eventthreshold has been exceeded by the event data and that the first eventtype has occurred. A. geographic boundary for an area associated withthe event data may be identified and may encompass a plurality ofsensors positioned in and around a plurality of houses. A response maybe automatically generated based on the first event type and thegeographic boundary. As a result, the efficiency and accuracy ofremedial reaction to a specific type of event may be improved, and/orimpact of the event on persons or belongings may be alleviated. Themethod may include additional, fewer, or alternative actions, includingthose discussed elsewhere herein.

In yet another aspect, a computer-implemented method for generating anautomated response to a catastrophic event may be provided. The methodmay include analyzing a sample set of data generated in association witha catastrophic event to determine a threshold pattern. Home sensor datamay also be collected/received from a smart home controller via wirelesscommunication or data transmission. The home sensor data may includedata regarding at least one of (i) structural status; (ii) wind speed;(iii) availability of electricity; (iv) presence of water; (v)temperature; (vi) pressure; and/or presence of pollutants in the airand/or water. The home sensor data may be analyzed with reference to thethreshold pattern to determine whether a match is present (indicating acatastrophic event has occurred or may have occurred). If a match ispresent, a response may be automatically generated. As a result, datagathering regarding a catastrophic event may be improved, preferablythrough optimization for an event type and use of “private” sensornetworks in place of or in addition to public emergency notificationsystems. The method may include additional, fewer, or alternativeactions, including those discussed elsewhere herein.

Advantages of these and other embodiments will become more apparent tothose skilled in the art from the following description of the exemplaryembodiments which have been shown and described by way of illustration.As will be realized, the present embodiments described herein may becapable of other and different embodiments, and their details arecapable of modification in various respects. Accordingly, the drawingsand description are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The Figures described below depict various aspects of devices andmethods disclosed therein. It should be understood that each Figuredepicts an embodiment of a particular aspect of the disclosed devicesand methods, and that each of the Figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingFigures, in which features depicted in multiple Figures are designatedwith consistent reference numerals. The present embodiments are notlimited to the precise arrangements and instrumentalities shown in theFigures.

FIG. 1 illustrates an exemplary system, constructed in accordance withvarious embodiments, and including a computing device configured toreceive data from a plurality of sensors through a communicationnetwork;

FIG. 2 illustrates a plurality of exemplary sensors that may be usedwith the system of FIG. 1;

FIG. 3 illustrates various components of the computing device shown inblock schematic form; and

FIG. 4 illustrates at least a portion of the steps of an exemplarycomputer-implemented method for generating an automated response to acatastrophic event may be provided.

The Figures depict exemplary embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the systems and methodsillustrated herein may be employed without departing from the principlesof the invention described herein.

DETAILED DESCRIPTION

The present embodiments described in this patent application and otherpossible embodiments address a computer-centric challenge or problemwith a solution that is necessarily rooted in computer technology andmay relate to, inter alia, devices and methods for tracking and/orgenerating automated responses to a catastrophic event using a remotesensor network. A plurality of sensors may be installed in and around ahomeowner's house. The sensors may include motion and/or glass breakdetectors, contact sensors, door lock keypads, thermostats, securitysystems, anemometers, barometric pressure sensors, water sensors, airand/or water pollution detectors, and the like. The sensors may recorddata regarding the status of the house—such as glass breaks, electricaloutages and/or other structural happenings, or the presence ofwater/flooding and/or other internal conditions—and/or physicalmeasurements of external weather conditions and air and/or waterpollution. In some embodiments, the sensors may detect externaltraffic-related conditions such as the presence of vehicles nearby.

The data may be transmitted from the sensors to a central hub whichforwards the data to a computing device. At various intervals, thecomputing device may compare the data against pre-established thresholdsand/or patterns indicative of the existence of one or more catastrophicevents. The computing device may also receive data from externaldatabases, such as weather tracking systems maintained by news servicesand/or the National Weather Service, which may also or alternatively becompared against pre-established thresholds, patterns and/or the sensordata to improve assessment of the potential catastrophic event(s). Theresults of the foregoing may result in reconfiguration of sensoroperation and/or data gathering patterns to improve data clarity arounda perceived event. The results of the foregoing may also oralternatively result in automated responses, such as generating a listof affected person(s) and/or recommending that an investigative team bedispatched to a specific locale.

Exemplary Computing System

FIG. 1 depicts an exemplary environment in which embodiments of acomputing device 10 for tracking and/or generating automated responsesto catastrophic events may be utilized. The computing device 10 mayreceive data from a plurality of sensors 12 that are installed in andaround a homeowner's house. The sensors 12 may transmit the data to acentral hub 14, which in turn, transmits the data to the computingdevice 10 through a communication network 16.

The sensors 12 may be distributed in and around the homeowner's house ormay be carried by the homeowner or other residents of the house.Exemplary sensors 12 are shown in FIG. 2, but may include other sensorsknown for detecting physical states, properties and/or changes that arerelevant to detection of a catastrophic event. Such sensors 12 mayinclude glass break sensors installed on a window or wall to detect whenglass is broken, thermostats that detect and set the temperature withinthe house, anemometers for detecting wind speed, water alarms fordetecting the presence and/or level of water, current sensors or similarsensors that detect the availability of electricity on a house'scircuit(s), infrared or other motion sensors, sensors for detecting thepresence of vehicles, air and/or water pollutant detectors, and/or othersimilar sensors for detecting structural, weather, electrical and/ortraffic-related information.

The sensors 12 each may include communications hardware that allow thesensor 12 to communicate, either through wires or wirelessly, with thecentral hub 14, which is typically located within the house. In otherembodiments, the central hub 14 may not be utilized and each sensor 12may communicate directly with the communication network 16.

Each sensor 12 may record, for example with a (time of day) timestamp,when activity occurred, such as when a wind condition occurred, when aglass break was detected, when electricity was lost from a circuit, andthe like. After the event occurs, or at predetermined time intervals,the sensor 12 may transmit event data, such as in a data packet, whichincludes, for example, an identification of the sensor 12, a timestampof when the activity occurred, and an indication of the activityrecorded and its magnitude, where applicable (e.g., “wind speed” and “12mph,” respectively). This event data may be transmitted to the centralhub 14 or to the computing device 10 (such as transmitting the eventdata to an insurance provider remote server for analysis against anevent threshold to determine if an event has occurred and/or toestablish a data receipt pattern).

The central hub 14 may include a plurality of ports (wired, wireless, orboth) configured to receive data from the sensors 12 and at least oneoutput port configured to transmit data to the communication network 16.The central hub 14 may further include buffering or other temporary datastorage capabilities. As soon as the central hub 14 receives data fromthe sensors 12, or at predetermined intervals, the central hub 14 maytransmit the data to the computing device 10 through the communicationnetwork 16. The central hub 14 in certain embodiments comprises a smarthome controller.

The communication network 16 generally allows communication between thehub 14 and the computing device 10 or communication directly from thesensors 12 to the computing device 10. The communication network 16 mayinclude local area networks, metro area networks, wide area networks,cloud networks, the Internet, and the like, or combinations thereof. Thecommunication network 16 may be wired, wireless, or combinations thereofand may include components such as switches, routers, hubs, accesspoints, and the like. The sensors 12 may connect to the communicationnetwork 16 either through wires, such as electrical cables or fiberoptic cables, or wirelessly, such as radio frequency (RF) communicationusing wireless standards such as Bluetooth® or the Institute ofElectrical and Electronic Engineers (IEEE) 802.11. In one embodiment,the sensors 12 may be wireless RF communication with each other or othersensors, with the hub 14 or smart home controller, communication network16, external computing devices 10 or remote servers, user mobile devices(e.g., smart phones, smart watches, wearable electronics, etc.), and/orsmart vehicles over radio links, nodes, or access points.

The computing device 10 may be embodied by workstation computers,desktop computers, laptop computers, palmtop computers, notebookcomputers, tablets or tablet computers, application servers, databaseservers, file servers, web servers, or the like, or combinationsthereof. The computing device 10, as shown in FIG. 3, may broadlycomprise a communication element 18, a memory element 20, and aprocessing element 22.

The communication element 18 generally allows the computing device 10 toreceive data from the communication network 16. The communicationelement 18 may include signal or data transmitting and receivingcircuits, such as antennas, amplifiers, filters, mixers, oscillators,digital signal processors (DSPs), and the like. The communicationelement 18 may establish communication wirelessly by utilizing RFsignals and/or data that comply with communication standards such ascellular 2G, 3G, or 4G, IEEE 802.11 standard such as WiFi, IEEE 802.16standard such as Wi MAX, Bluetooth™, or combinations thereof. Inaddition, the communication element 18 may utilize communicationstandards such as ANT, ANT+, Bluetooth™ low energy (BLE), theindustrial, scientific, and medical (ISM) band at 2.4 gigahertz (GHz),or the like. Alternatively, or in addition, the communication element 18may establish communication through connectors or couplers that receivemetal conductor wires or cables which are compatible with networkingtechnologies such as Ethernet. In certain embodiments, the communicationelement 18 may also couple with optical fiber cables. The communicationelement 18 may be in communication with the processing element 22 andthe memory element 20.

The memory element 20 may include electronic hardware data storagecomponents such as read-only memory (ROM), programmable ROM, erasableprogrammable ROM, random-access memory (RAM) such as static RAM (SRAM)or dynamic RAM (DRAM), cache memory, hard disks, floppy disks, opticaldisks, flash memory, thumb drives, universal serial bus (USB) drives, orthe like, or combinations thereof. In some embodiments, the memoryelement 20 may be embedded in, or packaged in the same package as, theprocessing element 22. The memory element 20 may include, or mayconstitute, a “computer-readable medium.” The memory element 20 maystore the instructions, code, code segments, software, firmware,programs, applications, apps, services, daemons, or the like that areexecuted by the processing element 22. The memory element 20 may alsostore settings, data, documents, sound files, photographs, movies,images, databases, and the like.

The processing element 22 may include electronic hardware componentssuch as processors, microprocessors (single-core and multi-core),microcontrollers, digital signal processors (DSPs), field-programmablegate arrays (FPGAs), analog and/or digital application-specificintegrated circuits (ASICs), or the like, or combinations thereof. Theprocessing element 22 may generally execute, process, or runinstructions, code, code segments, software, firmware, programs,applications, apps, processes, services, daemons, or the like. Theprocessing element 22 may also include hardware components such asfinite-state machines, sequential and combinational logic, and otherelectronic circuits that can perform the functions necessary for theoperation of the current invention. The processing element 22 may be incommunication with the other electronic components through serial orparallel links that include address busses, data busses, control lines,and the like.

The processing element 22 may be configured or programmed to perform thefollowing functions through hardware, software, firmware, orcombinations thereof. The processing element 22, through thecommunication element 18, may receive data from either the central hub14 and/or the sensors 12. The processing element 22 may analyze the datareceived from the house (such as from a hub comprising a smart homecontroller) with the home owner's or resident's permission oraffirmative consent.

At predetermined time intervals or upon receipt, the processing element22 may parse, organize, analyze and/or summarize the event data. Forexample, every minute, hour, day, week, and/or month, the processingelement 22 may analyze event data regarding the changing conditions andhappenings in and around the houses by comparing it against eventthreshold(s) to determine whether the data indicate a catastrophic eventmay have occurred or is likely to occur. The processing element 22 mayalso or alternatively analyze the event data to determine whetheradditional clarity may be needed around an event or possible event, forexample where additional and/or different event data may be requiredfrom the sensors 12 to track or confirm occurrence of an event.

If additional clarity is needed, the processing element 22 may determinea data receipt pattern based on the event data that is optimized toimprove tracking and assessment of an event or possible event. Forexample, the processing element 22 may determine which event type(s) aremost likely occurring or are most likely to occur by analyzing the eventdata, and may then determine a data profile or data receipt patternbased on what information is most likely to be useful going forward inthe event analysis process. In an embodiment, event data may indicatethe occurrence or likely occurrence of a first event type, which may inturn lead to more frequent data gathering/transmission and/orprioritization of one type of data over another, with the optimized setof such parameters for a given event type being referred to as the “datareceipt pattern.” This data receipt pattern may then lead toinitialization of a data file for receiving such data, and the receiptof such data in the file. The data received according to the datareceipt pattern, alternatively referred to herein as a “second set” ofevent data for the sake of clarity, is preferably sensor data receivedfrom a plurality of sensors situated in and around a plurality ofstructures, such as houses.

In addition to initializing a data file and receiving data according tothe data receipt pattern, the processing element 22 may generateoperational instructions for the sensor(s) 12 based on the analysis ofevent data. For example, the sensor(s) 12 may be instructed on a newconfiguration and/or parameter/setting that will enable the collectionof the data required by the data receipt pattern. The operationalinstructions may be formatted for direct transmission to the sensor(s)12 via communication network 16. The operational instructions may alsobe formatted as a request to an operator of the sensor(s) 12, forexample in a requirements format specifying time, frequency, data typeand other parameters that an operator such as a home alarm company mayuse to adjust the sensor(s) 12 and/or data collection methodsaccordingly. Likewise, event data from sensor(s) 12 may be provided viaan operator such as a home alarm company without departing from thespirit of the present inventive concept. Preferably, metadata regardingcertain types of event data is also provided, for example data regardingthe construction of a house and/or the placement therein with referenceto its layout, by the operator of the sensor(s) 12 to provide contextfor interpretation of event data.

Event thresholds may be set by the processing element 22 by correlatingone or more properties of event data preferably as derived from a sampleset of data with one or more event type(s). For example, a first eventtype may have one or multiple threshold(s) indicative of its occurrence.A hurricane event type may be indicated by a wind speed event thresholdand a barometric pressure event threshold. Alternatively, a hurricaneevent type may be indicated by a single event threshold comprising acombination of these two properties. An electrical outage event type maybe indicated by a single event threshold premised on a single propertyof the event data, such as a measure of electrical availability in ahouse. In this way, event threshold(s) may be set with reference to oneor more event types and may be pre-set for periodic comparison againstevent data to determine if such event(s) are occurring or are likely tooccur.

Event threshold(s) may also be finely tuned by the processing element 22into a threshold pattern with reference to a particular collection ofsensor(s) 12. For example, where a network of sensor(s) 12 of known typeand location are included in a sensor network, it may be possible toanalyze prior data from that area collected during occurrence of a priorevent to determine a more customized threshold pattern for that eventtype. The threshold pattern should, of course, allow for error andnatural variation, but should otherwise be finely tuned to recognize aseries and set of conditions that commonly unfold during the occurrenceof particular event type(s) in that area.

The processing element 22 may also access weather data, for exampleaverage wind speed data in tornadic conditions, from a databaseconfigured to store such data. The processing element 22 may use theweather data in developing event threshold(s) and/or thresholdpattern(s), may use the weather data as event data for comparisonagainst already-set event threshold(s), and/or may use the weather datato confirm or reject a conclusion that home sensor event data indicatesthe occurrence or likely future occurrence of a catastrophic event.

Preferably, processing element 22 will improve data collection andanalysis by also identifying a geographic boundary for an areaassociated with event data triggering one or more event threshold(s).The boundary may encompass areas that appear to be or are likely to beaffected by an event of the type indicated by the threshold(s), and mayinclude additional space along its borders to allow for errors inprediction, such as errors in predicting wind pattern and direction ofstorm movement. Identifying the boundary may assist in developing a datareceipt pattern, threshold pattern and/or operational instructions forthe sensors 12. Identifying the boundary may also or alternativelyinform the contents of an automated response, for example by identifyingan area predicted to be affected by the event and/or proposing an areato which response personnel should be traveling. Further, identifying aboundary may permit identification of individual resident(s) in thearea, such as customers holding insurance policies, who may need to becontacted regarding potential damage and/or insurance claims or thelike. Moreover, the processing element 22 may receive data regardinginsurance claims made by customer(s) in the area, and maycross-reference event data against such claims to detect fraud and/orprioritize response(s).

The processing element 22 may further receive metadata about the house,such as from other databases. For example, processing element 22 mayreceive such information from databases storing information entered inconnection with an application for home owners insurance or the like.The processing element 22 may receive information about features of thehouse such as the address of the house, the age of the house, theconstruction materials of the house, the layout of the house, the numberof bedrooms and bathrooms, and the like. This information may be usefulin providing context for an interpretation of home sensor-derived eventdata. The processing element 22 may also receive information about thehousehold or determine characteristics of the household from analysis ofthe data (e.g., image, audio, infrared, motion, and sensor data)received from the home (such as from the hub 14 or from another smarthome controller)

Although the preferred embodiments relate to generating, collecting, andanalyzing home sensor data at the processor element 22, the processorelement 22 may receive and analyze other types of data as well. Forinstance, sensor data (such as telematics data) may be generated byvehicle-mounted sensors and collected by a smart vehiclecontroller/processors, remotely received over one or more RF radio linksat the external computing device 10 over a communication network 16.Additionally or alternatively, sensor data (including but limited tosensor data) may be generated by mobile device-mounted sensors (e.g.,sensors or cameras within a smart phone) and remotely received orcollected over one or more RF radio links at the external computingdevice 10 over a communication network 16.

In addition to the foregoing, once the home-mounted sensor data,vehicle-mounted sensor data, mobile device sensor data, and/or otherdata (such as weather forecast or weather data) is received at theexternal computing device 10 via one or more radio links and thecommunication network 16, the external computing device 10 may apply oneor more machine learning, object recognition, or optical characterrecognition techniques on the data to determine (1) that the sensor orother event data indicates that an event threshold has been exceeded orthat an event exists; (2) a geographic boundary for an area impacted bythe event (which may be based upon GPS (Global Positioning System) dataor coordinates); and/or (3) a corrective action or response to theevent, or the estimated or actual extent of the event.

For instance, a machine learning program (or object recognition program)residing on a memory associated with the computing device 10 may betrained to determine actual or estimated extent of a hurricane or otherweather event, or other disaster, based upon historical image and sensordata (such as previous or historical home, mobile device, or vehiclesensor data received before, during, and after events). After which,newly received home, mobile device, or vehicle sensor data may be inputby the computing device 10 into the trained machine learning program todetermine (1) that an event threshold has been exceeded or that an eventexists; (2) a geographic boundary for an area impacted by the event;and/or (3) a corrective action or response to the event (such asgenerate and transmit notifications to user mobile devices (i)recommending that they seek shelter, move out of the way of the event(for a moving whether event); or take alternate routes avoiding the areaimpacted by the event if traveling by vehicle; and/or (ii) determine anestimated amount of damage to insured assets within the area impacted bythe event and prepared a proposed insurance claim for the insured'sreview and approval via their mobile device).

Exemplary Computer-Implemented Method

FIG. 4 depicts a listing of steps of an exemplary computer-implementedmethod 1.00 for generating an automated response to a catastrophicevent. The steps may be performed in the order shown in FIG. 4, or theymay be performed in a different order. Furthermore, some steps may beperformed concurrently as opposed to sequentially. In addition, somesteps may be optional. The steps of the computer-implemented method 100may be performed by the computing device 10.

Referring to step 101, an event threshold and/or threshold pattern maybe determined and set in association with one or more event types by theprocessing element 22. Event thresholds may be set by correlating one ormore properties of event data preferably as derived from a sample set ofdata with one or more event type(s). A sample data set may include datagenerated by a plurality of sensors 12. Alternatively or in addition, asample data set may be received from an external database, such as adatabase storing weather-related data from a previous occurrence of acatastrophic event. For example, a first event type may have one ormultiple threshold(s) indicative of its occurrence. A hurricane eventtype may be indicated by a wind speed event threshold and a barometricpressure event threshold. Alternatively, a hurricane event type may beindicated by a single event threshold comprising a combination of thesetwo properties, such as a weighted sum of the two properties. A volcanicevent may be indicated by an air and/or water pollutant eventthreshold(s). An electrical outage event type may be indicated by asingle event threshold premised on a single property of the event data,such as a measure of electrical availability in a house's circuit(s). Inthis way, event threshold(s) may be set for one or more event types andmay be pre-set for comparison against event data to determine if suchevent(s) are occurring or are likely to occur.

Event threshold(s) may also be finely tuned by the processing element 22into a threshold pattern with reference to a particular collection ofsensor(s) 12. For example, where a network of sensor(s) of known typeand location are included in a sensor network, it may be possible toanalyze prior data from that area collected during occurrence of a priorevent type to determine a threshold pattern for event data. Thethreshold pattern should, of course, allow for error and naturalvariation, but should otherwise be finely tuned to recognize a seriesand set of conditions that commonly unfold during the occurrence ofparticular event type(s) in that area.

Weather data accumulated from other sources may also be used, forexample data regarding average wind speed in tornadic conditions aspublished by the National Weather Service and/or otherwise obtained froma database configured to store such data. The weather data may be usedin developing event threshold(s) and/or threshold pattern(s), may beused as event data for comparison against already-set eventthreshold(s), and/or may be used to confirm or reject a conclusion thathome sensor event data indicates the occurrence or likely futureoccurrence of a catastrophic event.

Referring to step 102, event data may be received from a network ofsensors 12 or from an external source, such as a database for storingweather-related data. Sensors 12 may be distributed in and aroundhomeowners' houses. Each sensor 12 may record, for example with a (timeof day) timestamp, when activity occurred, such as when a wind conditionoccurred, when a glass break was detected, when electricity was lostfrom a circuit, and the like. After the event occurs, or atpredetermined time intervals, the sensor 12 may transmit event data,such as in a data packet, which includes, for example, an identificationof the sensor 12, a timestamp of when the activity occurred, and anindication of the activity recorded and its magnitude, where applicable(e.g., “wind speed” and “12 mph,” respectively). This event data may betransmitted to central hub 14 or to the computing device 10 (such astransmitting the event data to an insurance provider remote server).

The sensors 12 each may include communications hardware that allow thesensor 12 to communicate, either through wires or wirelessly, with thecentral hub 14, which is typically located within the house. In otherembodiments, the central hub 14 may not be utilized and each sensor 12may communicate directly with the communication network 16 over one ormore radio links. The central hub 14 may include a plurality of ports(wired, wireless, or both) configured to receive data from the sensors12 and at least one output port configured to transmit data to thecommunication network 16 (over one or more radio links). The central hub14 may further include buffering or other temporary data storagecapabilities. As soon as the central hub 14 receives data from thesensors 12, or at predetermined intervals, the central hub 14 maytransmit the data to the computing device 10 through the communicationnetwork 16 over one or more radio links. The central hub 14 in certainembodiments comprises a smart home controller. The communication network16 generally allows communication between the hub 14 and the computingdevice 10 or communication directly from the sensors 12 to the computingdevice 10, over one or more radio links. The sensors 12 may connect tothe communication network 16 either through wires, such as electricalcables or fiber optic cables, or wirelessly (over one or more radiolinks). Event data from sensor(s) 12 may also or alternatively beprovided via an operator such as a home alarm company without departingfrom the spirit of the present inventive concept.

Referring to step 103, event data may be analyzed with reference to theevent threshold and/or threshold pattern to determine that the thresholdhas been exceeded. For example, event data from a sensor 12 and/or froma weather-related database may indicate a particular wind speed wasrecorded at a specified location. This wind speed event data may becompared periodically or upon receipt by a computing device 10 with anevent threshold including the property of wind speed as being indicativeof at least one event type, alone or in combination with otherproperties. If the recorded wind speed from the event data meets orexceeds such an event threshold, the computing device 10 may determinethat a receipt pattern, operational instructions for the sensors 12,and/or an automated response should be generated, and/or it may seek toconfirm the determination using a “supplemental,” and preferablyindependent, source of information as described in further detail below.

Preferably, metadata regarding certain types of event data is alsocollected, for example data regarding the construction of a house and/orsensor placement therein with reference to its layout. This may help toprovide context for interpretation of event data. For example, knowingthe direction a window is facing with reference to a wind gust, the typeof window installed, and perhaps the age of the structure, may inform acomparison of event data against event threshold(s), e.g., by providingcontext for analyzing event data indicating a glass break correlatedwith the wind gust. Such metadata may be collected from externaldatabases, such as databases managed by service and/or materialsproviders, home alarm companies, builders, and others.

Referring to step 104, data collection and analysis will preferably beimproved by also identifying a geographic boundary for an areaassociated with event data exceeding one or more event threshold(s). Theboundary may encompass areas that appear to be or are likely to beaffected by an event of the type indicated by the threshold(s), and mayinclude additional space along the borders of the boundary to allow forerrors in prediction, such as errors in predicting wind pattern anddirection of storm movement. Identifying the boundary may assist indeveloping a data receipt pattern and/or operational instructions forthe sensors 12. Identifying the boundary may also or alternativelyinform the contents of an automated response, for example by identifyingan area predicted to be affected by the event and/or proposing an areato which response personnel should be traveling. Further, identifying aboundary may permit identification of individual resident(s) in thearea, such as customers holding insurance policies, who may need to becontacted regarding potential damage and/or insurance claims or thelike. Moreover, data regarding insurance claims made by customer(s) inthe area may be received and cross-referenced against event data todetect fraud and/or prioritize response(s).

Referring to step 105, a data receipt pattern may be determined usingthe event data, with such determination preferably taking into accountthe geographic boundary determined in step 104 and a network of sensors12 encompassed thereby. The data receipt pattern may be determined basedon the event data, and may represent an optimal data-gathering approachfor improved tracking and assessment of a detected event or possibleevent. For example, it may be determined which event type(s) are mostlikely occurring or are most likely to occur by analyzing the eventdata, and a data profile or data receipt pattern may be determined basedon what information is most likely to be useful going forward in theevent analysis process for such event type(s). In an embodiment, eventdata may indicate the occurrence or likely occurrence of a first eventtype, which may in turn lead to more frequent datagathering/transmission and/or prioritization of one type of data overanother, and/or one type of sensor over another, with the optimized setof such parameters for a given event type being its preferred datareceipt pattern.

The data receipt pattern may then lead to initialization of a data filefor receiving such data, and the receipt of such data in the file atstep 106. Also or alternatively at step 106, operational instructionsfor the sensor(s) 12 may be generated based on the analysis of eventdata. For example, the sensor(s) 12 within the geographic boundarydetermined at step 104 may be instructed on a new configuration and/orparameter/setting that will enable the collection of the data requiredby the data receipt pattern. The operational instructions may beformatted for direct transmission to the sensor(s) 12 via communicationnetwork 16. The operational instructions may also be formatted as arequest to an operator of the sensor(s) 12, for example in arequirements format specifying time, frequency, data type and otherparameters that an operator such as a home alarm company may use toadjust the sensor(s) 12 and/or data collection methods accordingly.

Referring to step 107, a response to the catastrophic event may beautomatically generated, and may be embodied in a digital and/or printedreport detailing the response. The response may take the form of arecommendation for deployment of a team of investigators to the areawithin the geographic boundary determined in step 104, a list ofcustomers and/or insurance claims made within the geographic boundary,and/or a list of contact information for customers that may be affectedby the event.

One or more of the foregoing steps may optionally be implemented inconjunction with and/or through execution of a machine learning program.The machine learning program may include curve fitting, regression modelbuilders, convolutional or deep learning neural networks, Bayesianmachine learning techniques, or the like. The machine learning programmay associate patterns from home sensor data with known events to informgeneration of data receipt patterns and/or event threshold(s),iteratively improve the form and contents of responses and/oroperational instructions to sensors through analyzing data regarding theefficacy of such measures over multiple events, and the like.

Other types of algorithms may also be applied to the sensor, weather,and other data received—such as object recognition or optical characterrecognition techniques. In additional to home sensor data, vehiclesensor data and mobile device generated data may also be collected andinput to trained or other machine learning, object recognition, oroptical character recognition programs and techniques to identifyevents, geographic boundaries of events, and proposed or recommendedresponses to the events.

Exemplary Computer-Implemented Method for Generating an AutomatedResponse to a Catastrophic Event

In yet another aspect, a computer-implemented method for generating anautomated response to a catastrophic event may be provided. The methodmay include analyzing a sample set of data generated in association witha catastrophic event to determine a threshold pattern. Home sensor datamay also be collected/received from a smart home controller via wirelesscommunication or data transmission. The home sensor data may includedata regarding at least one of (i) structural status; (ii) wind speed;(iii) availability of electricity; (iv) presence of water; (v)temperature; (vi) pressure; and/or (vii) presence of pollutants in theair and/or water. The home sensor data may be analyzed with reference tothe threshold pattern to determine whether a match is present(indicating a catastrophic event has occurred or may have occurred). Ifa match is present, a response may be automatically generated. As aresult, data gathering regarding a catastrophic event may be improved,preferably through optimization for an event type and use of “private”sensor networks in place of or in addition to public emergencynotification systems. The method may include additional, fewer, oralternative actions, including those discussed elsewhere herein.

For instance, an automated response may rely on event data and ageographic boundary developed therefrom to issue a deployment schedulefor a team of investigators tasked with developing facts regardingactual or potential insurance claims. It may be suggested that the teambegin in the area(s) within the boundary that reported the most severehome sensor data for a particular catastrophic event. The team may thenbe directed to work backward toward the less severely-affected areas.

The schedule or other response may also be informed and/or adjusted byconsidering a supplemental data set, such as reports or data from aweather-tracking database that may help confirm occurrence of the eventand/or provide additional support for planning a deployment schedule.The schedule may also be informed by the results of prior deployments.For instance, previous severity analyses and responses within thegeographic boundary may have led to a first deployment schedule. Oncethe deployed team enters its findings from its investigations, however,the severity analyses may take such findings into account to develop abetter-configured deployment schedule for a future event.

In another example, an automated response may include generation of alist of person(s) residing within the geographic boundary and associatedcontact information, insurance claims for damages within the geographicboundaries, and/or recommendations for people to contact for additionalremote investigation.

In the embodiments using home sensor data to identify air or waterpollution at one or more homes, the sensor data indicating pollution mayindicate the extent of an event, such as the extent of a chemical or oilspill. For instance, a train carrying chemicals or oil may be derailedand leak the chemicals or oil into a water source, such as a river orcreek. The home mounted sensors may ultimately detect that the chemicalsor oil have seeped or leaked into the water supply. Unknown long-termevents or pollution, and the geographical extent thereof, may also bedetected. For instance, chemical leakage or smoke/air emissions from afactory may pollute the air or water in a given area or neighborhood.Home sensor data indicating air or water pollution from several homesmay be used to identify the extent or scope of the pollution or impactedarea. After which, notifications may be sent those homeowners, oremergency responders may be sent to the impacted area for clean up.

Exemplary Computing Device for Tracking a Catastrophic Event Using aRemote Sensor Network

In another aspect, a network computing device for tracking acatastrophic event using a remote sensor network may be provided. Thenetwork computing device may include a communication element, a memoryelement, and/or a processing element. The communication element mayreceive data generated by a plurality of sensors positioned in andaround a house. The memory element may be electronically coupled to thecommunication element and may store data and executable instructions.The processing element may be electronically coupled to thecommunication element and the memory element. The processing element maybe configured to determine that an event threshold has been exceeded,the event threshold being configured for comparison against the eventdata. The processing element may further be configured to determine adata receipt pattern based on the event data, to initialize a data fileto receive additional sensor data from the plurality of sensors based onthe data receipt pattern, and to receive the additional sensor data intothe data file. The network computing device may include additional,fewer, or alternate components and/or functionality, including thatdiscussed elsewhere herein.

The processing element may be further configured to issue operationalinstructions to at least some of the plurality of sensors based on thedata receipt pattern and via the communication element. The processingelement may be further configured to format the operational instructionsfor transmission to the plurality of sensors via communication network.The processing element may also or alternatively be further configuredto format the operational instructions for routing to an operator of theplurality of sensors.

Exemplary Computer-Readable Medium for Tracking a Catastrophic EventUsing a Remote Sensor Network

In yet another aspect, a computer-readable medium for tracking acatastrophic event using a remote sensor network may be provided. Thecomputer-readable medium may include an executable program storedthereon, wherein the program instructs a processing element of a networkcomputing device to perform the following: (1) determine that an eventthreshold has been exceeded, the event threshold being configured forcomparison against event data; (2) identify a geographic boundary for anarea associated with the event data, the boundary encompassing aplurality of sensors positioned in and around a plurality of houses; (3)determine a data receipt pattern based on the event data; initialize adata file, based on the data receipt pattern, to receive sensor datafrom the plurality of sensors; and/or (4) receive the sensor data intothe data file. The program stored on the computer-readable medium mayinstruct the processing element to perform additional, fewer, oralternative actions, including those discussed elsewhere herein.

For instance, the program may instruct the processing element to issueoperational instructions to at least some of the plurality of sensorsbased on the data receipt pattern and via the communication element. Theprogram may further instruct the processing element to format theoperational instructions for transmission to the plurality of sensorsvia communication network. The program may also or alternativelyinstruct the processing element to format the operational instructionsfor routing to an operator of the plurality of sensors.

Exemplary Functionality

The present embodiments may involve smart home technology and/or smarthome controller collecting and analyzing data collected fromhome-mounted and other sensors. The data may be analyzed locally orremotely (by one or more local or remote processors) as a sample dataset to determine and/or adjust one or more event threshold(s) orthreshold pattern(s). Such event data may also or alternatively beanalyzed to determine whether an event threshold or threshold patternhas been met, indicating occurrence or likely occurrence of acatastrophic event.

The smart home controller may transmit the data collected periodically,or upon request, to an insurance provider remote server for analysis.For instance, the smart home controller may wirelessly communicate thesensor and/or other data collected every twelve (12) to twenty-four (24)hours.

One embodiment may use data from home security systems, smart homes, andother connected devices. Such information may be gathered from sensorsand systems such as glass break sensors installed on a window or wall todetect when glass is broken, thermostats that detect and set thetemperature within the house, anemometers for detecting wind speed,water alarms for detecting the presence and/or level of water, currentsensors or similar sensors that detect the availability of electricityon a house's circuit(s), infrared or other motion sensors, sensors fordetecting the presence of vehicles, air and/or water pollutantdetectors, and/or other similar sensors for detecting structural,weather, electrical and/or traffic-related information. Throughcontinual collection and analysis, a model may be created to guidetracking and response to catastrophic events. This may be done usingeither supervised or unsupervised machine learning.

The solution may include: (1) setting an event threshold that is for afirst event type and that is configured for comparison against eventdata; (2) analyzing the event data to determine that the event thresholdhas been exceeded by the event data and that the first event type hasoccurred; (3) identifying a geographic boundary for an area associatedwith the event data, the boundary encompassing a plurality of sensorspositioned in and around a plurality of houses; and/or (4) automaticallygenerating a response based on the first event type and the geographicboundary. The solution may further include comparison of a set ofinsurance claims against a list of person(s) residing within thegeographic boundary. The solution may further include confirming theoccurrence of the first event type by comparison of the event dataand/or event threshold with a supplemental data set, the supplementaldata set including weather-related data received from an externaldatabase configured for weather tracking.

In another aspect, a non-transitory computer-readable medium with anexecutable program stored thereon may be provided for detecting andgenerating an automated response to a catastrophic event. The programmay instruct a processing element of a computing device to: (1) set anevent threshold that is for a first event type and that is configuredfor comparison against event data; (2) analyze the event data todetermine that the event threshold has been exceeded by the event data,and that the first event type has occurred; (3) identify a geographicboundary for an area associated with the event data, the boundaryencompassing a plurality of sensors positioned in and around a pluralityof houses; and/or (4) automatically generate a response based on thefirst event type and the geographic boundary.

In another aspect, a computing device for detecting and generating anautomated response to a catastrophic event may be provided. Thecomputing device may include a communication element configured toreceive event data generated by a plurality of sensors positioned in andaround a house. The computing device may further include a memoryelement electronically coupled to the communication element, the memoryelement configured to store data and executable instructions. Thecomputing device may further include a processing element electronicallycoupled to the communication element and the memory element. Theprocessing element may be configured to: (1) set an event threshold thatis for a first event type and that is configured for comparison againstthe event data; (2) analyze the event data to determine that the eventthreshold has been exceeded by the event data, and that the first eventtype has occurred; (3) identify a geographic boundary for an areaassociated with the event data, the boundary encompassing the pluralityof sensors; and/or (4) automatically generate a response based on thefirst event type and the geographic boundary. The computing device mayinclude additional, less, or alternate functionality, including thatdiscussed elsewhere herein.

For instance, the response may include the computing device (i)determining a GPS location of a customer based upon vehicle or mobiledevice GPS location; (ii) determining that the GPS location of thecustomer is within the geographic boundary for the area associated withthe event; and (iii) if so, generating an alternate route for thevehicle of the customer to take and transmitting the alternate route totheir vehicle to alleviate impact of the event on the customer and/ortheir vehicle. Additionally or alternatively, the response may includethe external computing device and/or associated transceiver: (i)determining that a customer vehicle is moving from analysis of sensordata received via wireless communication over a radio link; (ii)determining a GPS location of a customer (such as based upon thecustomer vehicle or mobile device GPS location) for the sensor datareceived; (iii) determining that the GPS location of the customervehicle is within the geographic boundary for the area associated withthe event; (iv) if so, generating an alternate route for the customervehicle to take to minimize impact of the event on the customer and/orcustomer vehicle; and/or (v) transmitting the alternate route such thatcustomer may drive the vehicle according to the alternate route, or thatthe vehicle may automatically divert to the alternate route with thecustomer's permission (such as in the case of an autonomous vehicle orself-driving vehicle). The computing device may generate other types ofresponses, including those discussed elsewhere herein.

In another aspect, a computer system for generating an automatedresponse to a catastrophic event may be provided. The computer systemmay include one or more processors and/or transceivers configured to:(1) analyze a sample set of data generated in association with acatastrophic event to determine a threshold pattern; (2) receive homesensor data from a smart home controller via wireless communication ordata transmission, the home sensor data including data regarding atleast one of (i) structural status; (ii) wind speed; (iii) availabilityof electricity; (iv) presence of water; (v) temperature; (vi) pressure;and/or (vii) presence of pollutants in the air and/or water; (3)determine based upon or from computer analysis of the home sensor data,whether the home sensor data indicates a match to the threshold pattern;and/or (4) automatically generate a response if the home sensor dataindicates a match to the threshold pattern.

The one or more processors and transceivers may further be configuredto: issue operational instructions to the smart home controller toreconfigure for collection of additional home sensor data. The one ormore processors and transceivers may also or alternatively be configuredto confirm the occurrence of the catastrophic event by analyzing asupplemental data set, the supplemental data set includingweather-related data received from an external database configured forweather tracking. The system may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

Exemplary Embodiments

The present embodiments may relate to using real-time regional homesensor data to better identify areas affected by catastrophes ornoncatastrophe losses. The solution assumes that real-time home sensordata is available. By monitoring real-time home sensor data (withcustomer permission) or receiving reports from vendors based onreal-time home sensor data, an insurance provider may identifyareas/policyholders that reported erratic home sensor data and perhapsceased reporting data (in this case, it should be verified that it's notsimply a power outage). Such data may be used to prioritize whichpolicyholders are contacted first and where the catastrophe team is sentto, and to verify reported claims against the home telematics data,and/or other data, such as sensor, image, and audio data.

The present embodiments may (1) collect sensor data (door/window/glasssensors, water sensor, temperature, air and/or water pollution, etc.)from a geographic region; (2) match the (home or vehicle) telematicsdata with known catastrophes (hurricane, wind/hail, earthquake, andbrushfire) and non-cat losses (theft); (3) identify sensor reportingpatterns with types of loss events and set thresholds based upon thepatterns (for instance, sensor data may be received from several hundredhomes, and if all or most of the home sensors are reporting abnormal ordangerous conditions, or if there is a spike of sensor data frommultiple homes indicating abnormal or dangerous conditions, an actualevent may be presumed to be occurring); and/or (4) when a real-timesensor reporting pattern matches a pre-determined threshold, it maytrigger an event. At this time some cross-checking with weather data maybe warranted. With the present embodiments, (5) a triggered event mayentail contacting policyholders in the affected area, supplying the CATresponse team with additional information, or notifying policyholders ofrecent occurrences in the area.

After the event has passed, the sensor data associated with, oracquired, before, during, and after the event may collected andanalyzed, such as via a machine learning or object recognitiontechnique, to determine an extent of damage to insured assets, such as ahome, vehicle, or personal belongings. A proposed insurance claim, basedupon the extent of damage determined or estimated, may be generated andtransmitted to the insured's mobile device via one or more radio linksfor their review and/or approval.

In one aspect, a computer-implemented method of generating proposedcorrective actions to insurance-related events may be provided. Themethod may include: (1) receiving, at one or more processors and/ortransceivers over one or more radio links, via wireless communication ordata transmission, a first set of data (such as weather data, and/orsensor data (such as home-mounted, vehicle-mounted, and/or mobiledevice-mounted sensor (or image) data, and/or home or vehicle telematicsdata); (2) inputting, at the one or more processors, the first set ofdata into a machine learning or object recognition program (or datapattern recognition technique or program) to determine or identify aninsurance-related event, or otherwise determining or identifying, at theone or more processors, the insurance-related event from computeranalysis of the first set of data received; (3) generating, at the oneor more processors, a geographic boundary of an area associated with theinsurance-related event, or an actual or forecast extent thereof fromcomputer analysis of the first set of data and/or by inputting the firstset of data into the machine learning or object recognition program (ordata pattern recognition technique or program) to determine thegeographic boundary of the event; (4) receiving, at the one or moreprocessors and/or transceivers over one or more radio links, viawireless communication or data transmission, a second set of data (suchas customer-specific data, including GPS or sensor data (such ashome-mounted, vehicle-mounted, and/or mobile device-mounted sensor (orimage) data, and/or home or vehicle telematics data); (5) determining,at the one or more processors, that a GPS location of a customer iswithin, or may be in proximity, to the geographic boundary or the event,the GPS location being determined from a GPS coordinates within thesecond set of data (such as GPS location of a smart home controller,smart vehicle, or mobile device); (6) if so, generating a response orcorrective action, at the one or more processors; and/or (7)transmitting, from the one or more processors and/or associatedtransceivers, the response to a smart home controller, vehiclecontroller, or mobile device of the customer using wirelesscommunication and over one or more radio links to alleviate impact ofthe insurance-related event on insureds and/or insured assets (such asvehicles or homes).

The method may be implemented via one or more local or remote processorsand transceivers, and/or via computer-executable instructions stored oncomputer-readable media or medium. The method may include additional,less, or alternate actions, including those discussed elsewhere herein.

For instance, the response or corrective action may be an electronicmessage that is transmitted to the customer's mobile device warning themof an impending insurance-related path, a path thereof, and an estimatedintensity thereof at their current GPS location. The response orcorrective action may be determining or estimating, at the one or moreprocessors, an extent of damage to customer or insured assets (such aspersonal belongings, homes, or vehicles) caused by the insurance-relatedevent based upon the first and/or second set of data, such as byinputting the first and/or second set of data received into a machinelearning, object recognition, or pattern recognition program that istrained using historical images to identify damage and/or the extentthereof. The one or more processors may be further configured togenerate a proposed virtual insurance claim for the customer using theextent of damage estimated using the first and/or second set of data,and/or transmit the proposed virtual insurance claim to the customer'smobile device for their review, approval, or modification.

The response or corrective action may be generating, at the one or moreprocessors, a request that emergency or EMS personnel be sent to the GPSlocation of the customer (such as the current GPS location of theirmobile device, home, or vehicle controller contained within the secondset of sensor data); and/or transmitting, from the one or moreprocessors and/or associated transceivers, over one or more radio linksvia wireless communication or data transmission to a computing device orremote server associated with a police or fire department, or hospital.

The response or corrective action may include (i) determining a GPSlocation of a customer based upon vehicle or mobile device GPS location;(ii) determining that the GPS location of the customer is within thegeographic boundary for the area associated with the event; and (iii) ifso, generating an alternate route for the vehicle of the customer totake and transmitting the alternate route to their vehicle to alleviateimpact of the event on the customer and/or their vehicle. Additionallyor alternatively, the response or corrective action may include, at anexternal computing device and/or associated transceiver: (i) determiningthat a customer vehicle is moving from analysis of sensor data receivedvia wireless communication over a radio link; (ii) determining a GPSlocation of a customer (such as based upon the customer vehicle ormobile device GPS location) for the sensor data received; (iii)determining that the GPS location of the customer vehicle is within thegeographic boundary for the area associated with the event; (iv) if so,generating an alternate route for the customer vehicle to take tominimize impact of the event on the customer and/or customer vehicle;and/or (v) transmitting the alternate route over one or more radio linkssuch that customer may drive the vehicle according to the alternateroute, or that the vehicle may automatically divert to the alternateroute with the customer's permission (such as in the case of anautonomous vehicle or self-driving vehicle).

In another aspect, a computer system for generating proposed correctiveactions to insurance-related events may be provided. The computer systemmay include one or more processors and/or transceivers configured to:(1) receive over one or more radio links, via wireless communication ordata transmission, a first set of data (such as weather data, and/orsensor data (such as home-mounted, vehicle-mounted, and/or mobiledevice-mounted sensor (or image) data, and/or home or vehicle telematicsdata); (2) input the first set of data into a machine learning or objectrecognition program (or data pattern recognition technique or program)to determine or identify an insurance-related event, or otherwisedetermining or identifying, at the one or more processors, theinsurance-related event from computer analysis of the first set of datareceived; (3) generate a geographic boundary of an area associated withthe insurance-related event, or an actual or forecast extent thereoffrom computer analysis of the first set of data and/or by inputting thefirst set of data into the machine learning or object recognitionprogram (or data pattern recognition technique or program) to determinethe geographic boundary of the event; (4) receive over one or more radiolinks, via wireless communication or data transmission, a second set ofdata (such as customer-specific data, including GPS or sensor data (suchas home-mounted, vehicle-mounted, and/or mobile device-mounted sensor(or image) data, and/or home or vehicle telematics data); (5) determinethat a GPS location of a customer is within, or may be in proximity, tothe geographic boundary or the event, the GPS location being determinedfrom a GPS coordinates within the second set of data (such as GPSlocation of a smart home controller, smart vehicle, or mobile device);(6) if so, generate a response or corrective action; and/or (7) transmitthe response to a smart home controller, vehicle controller, or mobiledevice of the customer using wireless communication and over one or moreradio links to alleviate impact of the insurance-related event oninsureds and/or insured assets (such as vehicles or homes).

The computer system may include additional, less, or alternatefunctionality. For instance, the response or corrective action may be anelectronic message that is transmitted to the customer's mobile devicewarning them of an impending insurance-related path, a path thereof, andan estimated intensity thereof at their current GPS location. Theresponse or corrective action may be determining or estimating, at theone or more processors, an extent of damage to customer or insuredassets (such as personal belongings, homes, or vehicles) caused by theinsurance-related event based upon the first and/or second set of data,such as by inputting the first and/or second set of data received into amachine learning, object recognition, or pattern recognition programthat is trained using historical images to identify damage and/or theextent thereof The one or more processors may be further configured togenerate a proposed virtual insurance claim for the customer using theextent of damage estimated using the first and/or second set of data,and/or transmit the proposed virtual insurance claim to the customer'smobile device for their review, approval, or modification.

The response or corrective action may include generating, at the one ormore processors, a request that emergency or EMS personnel be sent tothe GPS location of the customer (such as the current GPS location oftheir mobile device, home, or vehicle controller contained within thesecond set of sensor data); and/or transmitting, from the one or moreprocessors and/or associated transceivers, over one or more radio linksvia wireless communication or data transmission to a computing device orremote server associated with a police or fire department, or hospital.

The response or corrective action may include the one or more processorsbeing configured to (i) determine a GPS location of a customer basedupon vehicle or mobile device GPS location; (ii) determine that the GPSlocation of the customer is within the geographic boundary for the areaassociated with the event; and (iii) if so, generate an alternate routefor an autonomous vehicle or the customer to take and transmit thealternate route to their autonomous or other vehicle to alleviate impactof the event on the customer and/or their autonomous or other vehicle.Additionally or alternatively, the response or corrective action mayinclude, at an external computing device and/or associated transceiver:(i) determining that a customer vehicle is moving from analysis ofsensor data received via wireless communication over a radio link; (ii)determining a GPS location of a customer (such as based upon thecustomer vehicle or mobile device GPS location) for the sensor datareceived; (iii) determining that the GPS location of the customervehicle is within the geographic boundary for the area associated withthe event; (iv) if so, generating an alternate route for the customervehicle to take to minimize impact of the event on the customer and/orcustomer vehicle; and/or (v) transmitting the alternate route over oneor more radio links such that customer may drive the vehicle accordingto the alternate route, or that the vehicle may automatically divert tothe alternate route with the customer's permission (such as in the caseof an autonomous vehicle or self-driving vehicle).

ADDITIONAL CONSIDERATIONS

With the foregoing, an insurance customer may opt-in to a rewards,insurance discount, or other type of program. After the insurancecustomer provides their affirmative consent, an insurance providertelematics application and/or remote server may collect smart home,mobile device, vehicle, telematics and/or other data (including image oraudio data) associated with insured assets, including before, during,and/or after an insurance-related event. In return, risk-averse homeand/or vehicle owners may receive discounts or insurance cost savingsrelated to auto, home, life, renters, pet, and other types of insurancefrom the insurance provider.

In one aspect, sensor data may be collected or received by an insured'ssmart home, mobile device or smart vehicle, and/or an insurance providerremote server, such as via direct or indirect wireless communication ordata transmission from an application running on the insured's smarthome controller, mobile device or vehicle, after the insured or customeraffirmatively consents or otherwise opts-in to an insurance discount,reward, or other program. The insurance provider may then analyze thedata received with the customer's permission to provide benefits to thecustomer. As a result, risk-averse customers may receive insurancediscounts or other insurance cost savings based upon functionalityand/or technology discussed herein that may mitigate or prevent risk to(i) insured assets, such as vehicles or even homes, and/or (ii) insuredsand family members caused by insurance-related events.

In this description, references to “one embodiment”, “an embodiment”, or“embodiments” mean that the feature or features being referred to areincluded in at least one embodiment of the technology. Separatereferences to “one embodiment”, “an embodiment”, or “embodiments” inthis description do not necessarily refer to the same embodiment and arealso not mutually exclusive unless so stated and/or except as will bereadily apparent to those skilled in the art from the description. Forexample, a feature, structure, act, etc. described in one embodiment mayalso be included in other embodiments, but is not necessarily included.Thus, the current technology can include a variety of combinationsand/or integrations of the embodiments described herein.

Although the present application sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the description is defined by the words of the claims set forthat the end of this patent and equivalents. The detailed description isto be construed as exemplary only and does not describe every possibleembodiment since describing every possible embodiment would beimpractical. Numerous alternative embodiments may be implemented, usingeither current technology or technology developed after the filing dateof this patent, which would still fall within the scope of the claims.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Certain embodiments are described herein as including logic or a numberof routines, subroutines, applications, or instructions. These mayconstitute either software (e.g., code embodied on a machine-readablemedium or in a transmission signal) or hardware. In hardware, theroutines, etc., are tangible units capable of performing certainoperations and may be configured or arranged in a certain manner. Inexample embodiments, one or more computer systems (e.g., a standalone,client or server computer system) or one or more hardware modules of acomputer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) ascomputer hardware that operates to perform certain operations asdescribed herein.

In various embodiments, computer hardware, such as a processing element,may be implemented as special purpose or as general purpose. Forexample, the processing element may comprise dedicated circuitry orlogic that is permanently configured, such as an application-specificintegrated circuit (ASIC), or indefinitely configured, such as an FPGA,to perform certain operations. The processing element may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement the processingelement as special purpose, in dedicated and permanently configuredcircuitry, or as general purpose (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “processing element” or equivalents should beunderstood to encompass a tangible entity, be that an entity that isphysically constructed, permanently configured (e.g., hardwired), ortemporarily configured (e.g., programmed) to operate in a certain manneror to perform certain operations described herein. Consideringembodiments in which the processing element is temporarily configured(e.g., programmed), each of the processing elements need not beconfigured or instantiated at any one instance in time. For example,where the processing element comprises a general-purpose processorconfigured using software, the general-purpose processor may beconfigured as respective different processing elements at differenttimes. Software may accordingly configure the processing element toconstitute a particular hardware configuration at one instance of timeand to constitute a different hardware configuration at a differentinstance of time.

Computer hardware components, such as communication elements, memoryelements, processing elements, and the like, may provide information to,and receive information from, other computer hardware components.Accordingly, the described computer hardware components may be regardedas being communicatively coupled. Where multiple of such computerhardware components exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the computer hardware components. In embodimentsin which multiple computer hardware components are configured orinstantiated at different times, communications between such computerhardware components may be achieved, for example, through the storageand retrieval of information in memory structures to which the multiplecomputer hardware components have access. For example, one computerhardware component may perform an operation and store the output of thatoperation in a memory device to which it is communicatively coupled. Afurther computer hardware component may then, at a later time, accessthe memory device to retrieve and process the stored output. Computerhardware components may also initiate communications with input oroutput devices, and may operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processing elements thatare temporarily configured (e.g., by software) or permanently configuredto perform the relevant operations. Whether temporarily or permanentlyconfigured, such processing elements may constitute processingelement-implemented modules that operate to perform one or moreoperations or functions. The modules referred to herein may, in someexample embodiments, comprise processing element-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processing element-implemented. For example, at least some ofthe operations of a method may be performed by one or more processingelements or processing element-implemented hardware modules. Theperformance of certain of the operations may be distributed among theone or more processing elements, not only residing within a singlemachine, but deployed across a number of machines. In some exampleembodiments, the processing elements may be located in a single location(e.g., within a home environment, an office environment or as a serverfarm), while in other embodiments the processing elements may bedistributed across a number of locations.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer with a processing element andother computer hardware components) that manipulates or transforms datarepresented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus.

The patent claims at the end of this patent application are not intendedto be construed under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the claim(s).

Although the invention has been described with reference to theembodiments illustrated in the attached drawing figures, it is notedthat equivalents may be employed and substitutions made herein withoutdeparting from the scope of the invention as recited in the claims.

Having thus described various embodiments of the invention, what isclaimed as new and desired to be protected by Letters Patent includesthe following:
 1. A method implemented by a computing system having oneor more memories and one or more processors, the method comprising:receiving a set of event sensor data associated with a catastrophicevent; analyzing the set of event sensor data to identify thecatastrophic event and an event type of the catastrophic event;generating a geographic boundary of an area associated with thecatastrophic event by entering the set of event sensor data into a firstmachine learning program, the first machine learning model being trainedusing historical sensor data to determine an extent of an event, thegeographic boundary being associated with an actual or forecasted extentof the catastrophic event; receiving a set of customer sensor dataassociated with a customer; determining a location of the customer basedupon the set of customer sensor data; determining whether the locationof the customer is within the geographic boundary of the catastrophicevent; and generating a response based upon the event type and thedetermination of whether the location of the customer is within thegeographic boundary of the catastrophic event.
 2. The method of claim 1,wherein the response includes a warning of the catastrophic event,wherein the warning includes the geographic boundary of the catastrophicevent and an estimated intensity of the catastrophic event at thelocation of the customer.
 3. The method of claim 1, wherein the responseincludes an alternate route for a vehicle of the customer to take. 4.The method of claim 3, wherein the vehicle of the customer is anautonomous vehicle.
 5. The method of claim 1, further comprising:estimating an extent of damage to an asset associated with the customercaused by the catastrophic event by inputting the set of customer sensordata into a second machine learning program that is trained usinghistorical images to identify damage and the extent thereof.
 6. Themethod of claim 5, further comprising: generating a proposed virtualinsurance claim for the customer using the estimated extent of damage.7. The method of claim 1, wherein the response includes a request thatemergency response personnel be sent to the location of the customer. 8.The method of claim 7, further comprising: transmitting the request to acomputing device associated with an emergency response organization, theemergency response organization comprising at least one of a policedepartment, a fire department and a hospital.
 9. The method of claim 1,wherein determining a location of the customer comprises determining thelocation of the customer using GPS coordinates in the set of customersensor data.
 10. The method of claim 1, wherein the event type isassociated with an event threshold pattern, wherein analyzing the set ofevent sensor data comprises determining whether the set of event sensordata indicates a match to the event threshold pattern.
 11. A computingsystem comprising: one or more memories storing instructions; one ormore processors configured to execute the instructions to performoperations comprising: receiving a set of event sensor data associatedwith a catastrophic event; analyzing the set of event sensor data toidentify the catastrophic event and an event type of the catastrophicevent; generating a geographic boundary of an area associated with thecatastrophic event by entering the set of event sensor data into a firstmachine learning program, the first machine learning model being trainedusing historical sensor data to determine an extent of an event, thegeographic boundary being associated with an actual or forecasted extentof the catastrophic event; receiving a set of customer sensor dataassociated with a customer; determining a location of the customer basedupon the set of customer sensor data; determining whether the locationof the customer is within the geographic boundary of the catastrophicevent; and generating a response based upon the event type and thedetermination of whether the location of the customer is within thegeographic boundary of the catastrophic event.
 12. The system of claim11, wherein the response includes a warning of the catastrophic event,wherein the warning includes the geographic boundary of the catastrophicevent and an estimated intensity of the catastrophic event at thelocation of the customer.
 13. The system of claim 11, wherein theresponse includes an alternate route for a vehicle of the customer totake.
 14. The system of claim 13, wherein the vehicle of the customer isan autonomous vehicle.
 15. The system of claim 11, wherein theoperations further comprise: estimating an extent of damage to an assetassociated with the customer caused by the catastrophic event byinputting the set of customer sensor data into a second machine learningprogram that is trained using historical images to identify damage andthe extent thereof.
 16. The system of claim 15, wherein the operationsfurther comprise: generating a proposed virtual insurance claim for thecustomer using the estimated extent of damage.
 17. The system of claim11, wherein the response includes a request that emergency responsepersonnel be sent to the location of the customer.
 18. The system ofclaim 17, wherein the operations further comprise: transmitting therequest to a computing device associated with an emergency responseorganization, the emergency response organization comprising at leastone of a police department, a fire department and a hospital.
 19. Thesystem of claim 11, wherein determining a location of the customercomprises determining the location of the customer from GPS coordinatesin the set of customer sensor data.
 20. The system of claim 11, whereinthe event type is associated with an event threshold pattern, whereinanalyzing the set of event sensor data comprises determining whether theset of event sensor data indicates a match to the event thresholdpattern.